Environment Systems and Decisions

, Volume 39, Issue 3, pp 307–348 | Cite as

The provision and utility of science and uncertainty to decision-makers: earth science case studies

  • Mark C. QuigleyEmail author
  • Luke G. Bennetts
  • Patricia Durance
  • Petra M. Kuhnert
  • Mark D. Lindsay
  • Keith G. Pembleton
  • Melanie E. Roberts
  • Christopher J. White


This paper investigates how scientific information and expertise was provided to decision-makers for consideration in situations involving risk and uncertainty. Seven case studies from the earth sciences were used as a medium for this exposition: (1) the 2010–2011 Canterbury earthquake sequence in New Zealand, (2) agricultural farming-system development in North West Queensland, (3) operational flood models, (4) natural disaster risk assessment for Tasmania, (5) deep sea mining in New Zealand, (6) 3-D modelling of geological resource deposits, and (7) land-based pollutant loads to Australia’s Great Barrier Reef. Case studies are lead-authored by a diverse range of scientists, based either in universities, industry, or government science agencies, with diverse roles, experiences, and perspectives on the events discussed. The context and mechanisms by which scientific information was obtained, presented to decision-makers, and utilised in decision-making is presented. Sources of scientific uncertainties and how they were communicated to and considered in decision-making processes are discussed. Decisions enacted in each case study are considered in terms of whether they were scientifically informed, aligned with prevailing scientific evidence, considered scientific uncertainty, were informed by models, and were (or were not) precautionary in nature. The roles of other relevant inputs (e.g. political, socioeconomic considerations) in decision-making are also described. Here we demonstrate that scientific evidence may enter decision-making processes through diverse pathways, ranging from direct solicitations by decision-makers to independent requests from stakeholders following media coverage of relevant research. If immediately relevant scientific data cannot be provided with sufficient expediency to meet the demands of decision-makers, decision-makers may (i) seek expert scientific advice and judgement (to assist with decision-making under conditions of high epistemic uncertainty), (ii) delay decision-making (until sufficient evidence is obtained), and/or (iii) provide opportunities for adjustment of decisions as additional information becomes available. If the likelihood of occurrence of potentially adverse future risks is perceived by decision-makers to exceed acceptable thresholds and/or be highly uncertain, precautionary decisions with adaptive capacity may be favoured, even if some scientific evidence suggests lower levels of risk. The efficacy with which relevant scientific data, models, and uncertainties contribute to decision-making may relate to factors including the expediency with which this information can be obtained, the perceived strength and relevance of the information presented, the extent to which relevant experts have participated and collaborated in scientific communications to decision-makers and stakeholders, and the perceived risks to decision-makers of favouring earth science information above other, potentially conflicting, scientific and non-scientific inputs. This paper provides detailed Australian and New Zealand case studies showcasing how science actions and provision pathways contribute to decision-making processes. We outline key learnings from these case studies and encourage more empirical evidence through documented examples to help guide decision-making practices in the future.


Earth science Environmental science Decision-making Policy Natural disasters Risk Uncertainty 



The modelling conducted in case study 2 and analysis activities reported, and the development of the tools were made possible through funding provided by the Queensland Department of Agriculture and Fisheries under the Department of Agriculture and Fisheries—University of Southern Queensland Broad Acre Grains Partnership. Dr Lance Pedergast (Senior Development Agronomist with the Queensland Department of Agriculture and Fisheries) assisted in the design, analysis and communication of chickpea analysis presented in this case study. Mr Howard Cox (Senior Agronomist Department of Agriculture and Fisheries) has contributed to the design and development of the ARM online tools. The author of case study 5 (PD) would like to thank Ray Wood and Renee Grogan for discussing many aspects of the CRP application and EPANZ hearing and decision. Their comments and suggestion greatly improved the original document. Hamish Campbell is also acknowledged for providing a thorough review of the draft. The authors thank the Australian Academy of Science ( for providing the opportunity to participate in the 2016 Theo Murphy High Flyers Think Tank ( From this intellectually challenging and stimulating event, this multi-disciplinary authorship team emerged, with broadened perspectives that benefitted the authors’ collaborative effort to tackle the challenging topics that are presented herein.


  1. Apel H, Thieken A, Merz B (2006) A probabilistic modelling system for assessing flood risks. Nat Hazards. CrossRefGoogle Scholar
  2. Armour JD, Hateley LR, Pitt GL (2009) Catchment modelling of sediment, nitrogen and phosphorus nutrient loads with SedNet/ANNEX in the Tully-Murray basin. Mar Freshw Res 60:1091–1096CrossRefGoogle Scholar
  3. Ascough J II, Maierb H, Ravalico J, Strudley M (2008) Future research challenges for incorporation of uncertainty in environmental and ecological decision-making. Ecol Model 219:383–399CrossRefGoogle Scholar
  4. Australian Geomechanics Society (2007) Practice note guidelines for landslide risk management. J News Aust Geomech Soc 42:155–164Google Scholar
  5. Babcock B (1992) The effects of uncertainty on optimal nitrogen applications. Appl Econ Perpect Policy 14:271–280. CrossRefGoogle Scholar
  6. Barlow K, Thayalakumaran T, Moody P (2016) Extending “SafeGauge for Nutrients” to high rainfall cropping in Australia. In: 7th International Nitrogen Imitative Conference, Melbourne, Australia, 2016Google Scholar
  7. Barnes L, Gruntfest E, Hayden M, Schultz D, Benight C (2007) False alarms and close calls: a conceptual model of warning accuracy. Weather Forecast 22:1140–1147. CrossRefGoogle Scholar
  8. Bartley R, Speirs WJ, Ellis TW, Waters DK (2012) A review of sediment and nutrient concentration data from Australia for use in catchment water quality models. Marine Pollut Bull 65:101–116CrossRefGoogle Scholar
  9. Beaven S, Wilson T, Johnston L, Johnston D, Smith R (2016) Role of boundary organization after a disaster: New Zealand’s natural hazards research platform and the 2010–2011 canterbury earthquake sequence. Nat Hazards Rev 18:05016003CrossRefGoogle Scholar
  10. Becker J, Potter S, Wein A, Doyle E, Ratliff J (2015) Aftershock communication during the Canterbury earthquakes, New Zealand: implications for response and recovery in the built environment. In: New Zealand Society of Earthquake Engineering, 2015Google Scholar
  11. Behnam A, Visbeck M (2013) The future of fish–The fisheries of the future. Hamburg, GermanyGoogle Scholar
  12. Behnam A, Visbeck M (2014) Marine resources—Opportunities and risks. Hamburg, GermanyGoogle Scholar
  13. Bernecker T, Woollands M, Wong D, Moore D, Smith M (2001) Hydrocarbon prospectivity of the deep water Gippsland Basin, Victoria, Australia. APPEA J 41:91–113CrossRefGoogle Scholar
  14. Berryman K (2012) Geoscience as a component of response and recovery from the Canterbury earthquake sequence of 2010–2011. N Z J Geol Geophys 55:313–319CrossRefGoogle Scholar
  15. Bollmann Moritz et al (2010) Living with the oceans. Hamburg, GermanyGoogle Scholar
  16. Borella J, Quigley M, Sohbati R, Almond P, Gravley DM, Murray A (2016a) Chronology and processes of late Quaternary hillslope sedimentation in the eastern South Island. N Z J Quat Sci 31:691–712CrossRefGoogle Scholar
  17. Borella J, Quigley M, Vick L (2016b) Anthropocene rockfalls travel farther than prehistoric predecessors. Sci Adv 2:e1600969. CrossRefGoogle Scholar
  18. Boskalis Offshore (2014a) Appendix 3: compilation of literature on the Chatham Rise phosphorite depositGoogle Scholar
  19. Boskalis Offshore (2014b) Appendix 4: summary and analysis of site investigation dataGoogle Scholar
  20. Bosomworth B, Cowie B (2016) Response to the independent reviews of DNRM activities under Action 8 & 9 of Reef Plan 2013. Land and Water Science, Department of Natural Resources and Mines, State of QueenslandGoogle Scholar
  21. Breznitz S (1984) Cry wolf: the psychology of false alarms. Lawrence Erlbaum Associates, New JerseyGoogle Scholar
  22. Brodie J (2012) Great Barrier Reef dying beneath its crown of thorns. The ConversationGoogle Scholar
  23. Bruns A, Burgess J (2012) Local and global responses to disaster: #eqnz and the Christchurch earthquake. In: disaster and emergency management conference, Brisbane Exhibition and Convention Centre, AST Management Pty Ltd, Brisbane, QLD, pp 86–103Google Scholar
  24. Cheong L, Bleisch S, Kealy A, Tolhurst K, Wilkening T, Duckham M (2016) Evaluating the impact of visualization of wildfire hazard upon decision-making under uncertainty. Int J Geogg Inf Sci 30:1377CrossRefGoogle Scholar
  25. Chiswell SM (2014) Appendix 8: Physical oceanographic data available on the Chatham RisGoogle Scholar
  26. Clark JS (2005) Why environmental scientists are becoming Bayesians. Ecol Lett 8(1):2–14CrossRefGoogle Scholar
  27. Cloke H, Pappenberger F (2009) Ensemble flood forecasting: a review. J Hydrol 375:613–626. CrossRefGoogle Scholar
  28. Coates L, Haynes K, O’Brien J, McAneney J, de Oliveira F (2014) Exploring 167 years of vulnerability: an examination of extreme heat events in Australia 1844–2010. Environ Sci Policy 42:33–44. CrossRefGoogle Scholar
  29. Colyvan M et al. (2017) Addressing risk in conditions of uncertainty, ignorance, and partial knowledge. In: An interdisciplinary approach to living in a risky world. Recommendations from the Theo Murphy High Flyers Workshop, Australian Academy of Science, Canberra, ACT, pp. 5–7Google Scholar
  30. Comrie N (2011) Review of the 2010–11 flood warnings and response. Victorian Government, AustraliaGoogle Scholar
  31. Cox H, Hammer G, McLean G, King C National WhopperCropper – risk management discussion support software. In ‘New directions for a diverse planet. In: Fischer T, Turner N, Angus J, Mcintyre L, Robertson M, Borrell A, Lloyd D (eds) Proceedings for the 4th international crop science congress, Brisbane, Australia, 2004Google Scholar
  32. CRP (2014) Appendix 7: other voyages overviewGoogle Scholar
  33. Cubrinovski M et al (2010) Geotechnical reconnaissance of the 2010 Darfield (Canterbury) earthquake. Bull N Z Soc Earthq Eng 43:243–320Google Scholar
  34. Dalgliesh N, Cocks B, Horan H (2012) APSoil-providing soils information to consultants, farmers and researchers. In: capturing opportunities and overcoming obstacles in Australian Agronomy” Proceedings of the 16th ASA conference, Armidale, NSW Australia, 2012Google Scholar
  35. Darnell R, Henderson BL, Kroon FJ, Kuhnert PM (2012) Statistical power of detecting trends in total suspended sediment loads to the Great Barrier Reef. Mar Pollut Bull 65:203–209CrossRefGoogle Scholar
  36. De’Ath G, Fabricius KE, Sweatman H, Puotinen M (2012) The 27-year decline of coral cover on the Great Barrier Reef and its causes. Proc Natl Acad Sci USA 109:17995–17999CrossRefGoogle Scholar
  37. Dow K, Cutter S (1998) Crying wolf: repeat responses to hurricane evacuation orders. Coast Manage 26:237–252CrossRefGoogle Scholar
  38. Draper D (1995) Assessment and propagation of model uncertainty. J R Stat Soc 57:45–97Google Scholar
  39. Durden J et al (2016) Report on the managing impacts of deep-sea reSource exploitation (MIDAS) workshop on environmental management of deep-sea mining. Res Ideas Outcomes 2:e10292CrossRefGoogle Scholar
  40. Environmental Protection Authority NZ (2017) Decision on marine consents and marine discharge consents application: EEZ000011, Trans-Tasman Resources Limited. Extracting and processing ireon sand within the South Taranaki Bight, WellingtonGoogle Scholar
  41. Environmental Protection Authority NZ (2012) Exclusive economic zone and continental shelf environmental effects act 2012. The New Zealand Federal Government, New ZealandGoogle Scholar
  42. Environmental Protection Authority NZ (2015) Decision on marine consent application by Chatham Rock Phosphate Limited to mine phosphorite nodules on the Chatham Rise. Environmental Protection Authority, WellingtonGoogle Scholar
  43. Fergusson D, Horwood L, Boden J, Mulder R (2014) Impact of a major disaster on the mental health of a well-studied cohort. JAMA Psychiatry 71:1025–1031CrossRefGoogle Scholar
  44. Finkel, A. (1990) Confronting Uncertainty in Risk Management: A Guide for Decision Makers. Center for Risk Management, Resources for the FutureGoogle Scholar
  45. Fischer R (2009) Exploiting the synergy between genetic improvement and agronomy of crops in rainfed farming systems of Australia. In: Sadras V, Calderini D (eds) Crop physiology: applications for genetic improvement and agronomy. Elsevier, AmsterdamGoogle Scholar
  46. Fischhoff B, Davis AL (2014) Communicating scientific uncertainty. Proc Natl Acad Sci 111(Supplement 4):13664–13671CrossRefGoogle Scholar
  47. Gerstenberger M et al (2011) Probabilistic assessment of liquefaction potential for Christchurch in the next 50 years. vol 2011/15. GNS Science, Lower HuttGoogle Scholar
  48. Gjerde KM et al. (2016) Report on the implications of MIDAS results for policy makers with recommendations for future regulations to be adopted by the EU and the ISAGoogle Scholar
  49. Gladish DW et al (2016) Spatio-temporal assimilation of modelled catchment loads with monitoring data in the Great Barrier Reef. Ann Appl Stat 10:1590–1618CrossRefGoogle Scholar
  50. Gledhill K, Ristau J, Reyners M, Fry B, Holden C (2010) The Darfield (Canterbury) Mw 7.1 earthquake of September 2010: preliminary seismological report. Bull N Z Soc Earthq Eng 43:215–221Google Scholar
  51. Gluckman P (2014) The art of science advice to government. Nature 507:163CrossRefGoogle Scholar
  52. Golder Associates Ltd (2014a) Appendix 35(i): draft environmental management and monitoring planGoogle Scholar
  53. Golder Associates Ltd (2014b) Marine consent application and environmental impact assessmentGoogle Scholar
  54. Golder Associates Ltd (2014c) Marine Consent application and environmental impact assessment: non-technical summaryGoogle Scholar
  55. Grogan R (2017) Regulating extractive industries: what works in practice?, Practitioners’ perspectives on the effective implementation of environmental legislationGoogle Scholar
  56. Halfar J, Fujita RM (2002) Precautionary management of deep-sea mining. Mar Policy 26:103–106. CrossRefGoogle Scholar
  57. Hannington M, Petersen S, Kratschell A (2017) Subsea mining moves closer to shore. Nat Geosci 10:158–159. CrossRefGoogle Scholar
  58. Herron DA (2011) First steps in seismic interpretation. Geophysical monograph series number 16. Society of Exploration GeophysicistsGoogle Scholar
  59. Holzworth D et al (2014) APSIM—Evolution towards a new generation of agricultural systems simulation. Environ Modell Softw 62:327–350CrossRefGoogle Scholar
  60. Hughes T, Cinner J (2017) The world’s coral reefs. The ConversationGoogle Scholar
  61. Hughes M, Quigley M, van Ballegooy S, Deam B, Bradley B, Hart D (2015) The sinking city: earthquakes increase flood hazard in Christchurch, New Zealand. GSA Today 25:4–10CrossRefGoogle Scholar
  62. Hughes T et al (2017) Coral reefs in the anthropocene. Nature 546:82–90CrossRefGoogle Scholar
  63. Hughes-Allan S, Peters K, O’Donnell R (2014) Appendix 2: genesis of the Chatham Rise deposit: a synthesis of current literature (Kenex 2010)Google Scholar
  64. Jarihani B, Sidle RC, Bartley R, Roth CH, Wilkinson S (2017) Characterisation of hydrological response to rainfall at multi spatio-temporal scales in savannas of semi-arid Australia. Water 9:540CrossRefGoogle Scholar
  65. Jeffrey S, Carter J, Moodie K, Beswick A (2001) Using spatial interpolation to construct a comprehensive archive of Australian climate data. Environ Modell Softw 16:309–330CrossRefGoogle Scholar
  66. Jenkins L, Brill R The effect of time of sowing on phenology and yield of chickpeas at Trangie Central West, NSW, 2011. In: capturing opportunities and overcoming obstacles in Australian Agronomy, Proceedings of the 16th ASA Conference, Armidale, Australia, 2012Google Scholar
  67. Jones JWAJM, Basso B, Boote KJ, Conant RT, Foster I, Godfray HCJ, Herrero M, Howitt RE, Janssen S, Keating BA, Munoz RM, Porter CH, Rosenzweig C, Wheeler TR et al (2016) Brief history of agricultural systems modeling. Agric Syst 155:240–254. CrossRefGoogle Scholar
  68. Kroon FJ et al (2012) River loads of suspended solids, nitrogen, phosphorus and herbicides delivered to the Great Barrier Reef Lagoon. Mar Pollut Bull 65:167–181CrossRefGoogle Scholar
  69. Kroon FJ, Thorburn P, Schaffelke B, Whitten S (2016) Towards protecting the Great Barrier Reef from land-based pollution. Glob Change Biol. CrossRefGoogle Scholar
  70. Kuhnert PM, Henderson BL, Lewis SE, Bainbridge ZT, Wilkinson SN, Brodie JE (2012) Quantifying total suspended sediment export from the Burdekin River catchment using the loads regression estimator tool. Water Resour Res 48:256. CrossRefGoogle Scholar
  71. Kuhnert PM, Pagendam DE, Bartley R, Gladish DW, Lewis SE, Bainbridge ZT (2017) Making management decisions in the face of uncertainty: a case study using the Burdekin catchment in the Great Barrier Reef Marine and Freshwater Research SubmittedGoogle Scholar
  72. Leonard M et al (2014) A compound event framework for understanding extreme impacts WIREs. Clim Change 5:113–128. CrossRefGoogle Scholar
  73. Lescinski J (2014) Statement of evidence of Jamie Lescinski for Chatham Rock Phosphate Limited. EPANZ, WelllingtonGoogle Scholar
  74. Lindsay M, Aillères L, Jessell M, de Kemp E, Betts P (2012) Locating and quantifying geological uncertainty in three-dimensional models: analysis of the Gippsland Basin, southeastern Australia. Tectonophysics 546–547:10–27CrossRefGoogle Scholar
  75. Linkov I, Anklam E, Collier ZA, DiMase D, Renn O (2014) Risk-based standards: integrating top–down and bottom–up approaches. Environ Syst Decis 34:134–137CrossRefGoogle Scholar
  76. Mackey B, Quigley M (2014) Strong proximal earthquakes revealed by cosmogenic 3He dating of prehistoric rockfalls, Christchurch, New Zealand. Geology 42:975–978CrossRefGoogle Scholar
  77. Mann J (1993) Computers in geology—25 years of progress. In: Davis J, Herzfeld UZ (eds) Uncertainty in geology. Oxford University Press, Oxford, pp 241–254Google Scholar
  78. Martin H (1999) Adakitic magmas: modern analogues of Archaean granitoids. Lithos 46:411–429CrossRefGoogle Scholar
  79. Massey C et al (2014) Determining rockfall risk in Christchurch using rockfalls triggered by the 2010–2011 Canterbury earthquake sequence. Earthq Spectra 30:155–181CrossRefGoogle Scholar
  80. McDowell RW, Littlejohn RP, Blennerhassett JD (2010) Phosphorus fertilizer form affects phosphorus loss to waterways: a paired catchment study. Soil Use Manag 26:365–373. CrossRefGoogle Scholar
  81. McLean M, Blackburn G (2013) A new regional velocity model for the Gippsland Basin. Department of Primary IndustriesGoogle Scholar
  82. Moody P, Agustina L, Robinson B, McHugh A, Dang Y (2013) Safegauge: a web-based decision support tool for informing nutrient management in the Queensland sugar industry vol???Google Scholar
  83. Moore D, Wong D (2002) Eastern and Central Gippsland Basin, Southeast Australia; Basement Interpretation and Basin Links. Department of Natural Resources and Environment, AustraliaGoogle Scholar
  84. Morgan M, Henrion M (1990) Uncertainty. A guide to dealing with uncertainty in quantitative risk and policy analysis. Cambridge University Press, Cambridge, p 346CrossRefGoogle Scholar
  85. Nathan R, Weinmann E, Hill P Use of Monte Carlo Simulation to Estimate the Expected Probability of Large to Extreme Floods. In: Boyd M, Ball J, Babister M, Green J (eds) 28th International Hydrology and Water Resources Symposium: About Water, Barton, ACT, Australia, 2003. Institution of Engineers, AustraliaGoogle Scholar
  86. Neale T (2015) Scientific knowledge and scientific uncertainty in bushfire and flood risk mitigation. Bushfire and Natural Hazards Cooperative Research Centre, East MelbourneGoogle Scholar
  87. Parker M, Steenkamp D (2012) The economic impact of the Canterbury earthquakes. Reserve Bank N Z Bull 75:13–25Google Scholar
  88. Pechlivanidis I, Jackson B, McIntyre N, Wheater H (2011) Catchment scale hydrological modelling: a review of model types, calibration approaches and uncertainty analysis methods In the context of recent developments in technology and applications. Glob NEST J 13:193–214Google Scholar
  89. Pielke RA Jr (2003) The role of models in prediction for decision. In: Lauenroth WK (ed) Models in ecosystem science. Princeton University Press, Princeton, pp 111–135Google Scholar
  90. Pielke RA Jr, Conant RT (2003) Best practices in prediction for decision-making: lessons from the atmospheric and earth sciences. Ecology 84(6):1351–1358CrossRefGoogle Scholar
  91. QAO (2015) Managing water quality in Great Barrier Reef catchments vol Report 20: 2015-2015. Queensland Audit Office, The State of QueenslandGoogle Scholar
  92. Queensland Government (2009) Great Barrier Reef first report card 2009 baseline. Reef water quality protection plan. Queensland Government, BrisbaneGoogle Scholar
  93. Queensland Government (2015) Great Barrier Reef report card 2014. Reef water quality protection plan. Queensland Government, BrisbaneGoogle Scholar
  94. Quigley M, Forte A (2017) Science website traffic in earthquakes. Seismol Res Lett. CrossRefGoogle Scholar
  95. Quigley M et al (2010) Surface rupture of the Greendale fault during the Darfield (Canterbury) earthquake, New Zealand: initial findings. Bull N Z Soc Earthq Eng 43:236–243Google Scholar
  96. Quigley M, Bastin S, Bradley B (2013) Recurrent liquefaction in Christchurch, New Zealand, during the Canterbury earthquake sequence. Geology 41:419–422CrossRefGoogle Scholar
  97. Quigley M et al (2016) The 2010–2011 Canterbury earthquake sequence: environmental effects, seismic triggering thresholds and geologic legacy. Tectonophysics 672–673:228–274CrossRefGoogle Scholar
  98. Quigley, M et al. (in review at Minerva) The provision and utility of earth science to decision-makersGoogle Scholar
  99. Rahmanian V, Moore P, Mudge W, Spring D (1990) Sequence stratigraphy and the habitat of hydrocarbons, Gippsland Basin, Australia, vol 50. Geological Society, LondonGoogle Scholar
  100. Recommendations from the 2016 Theo Murphy High Flyers Think Tank. An interdisciplinary approach to living in a risky world. (2017). Canberra: Australian Academy of Science.
  101. Reef Water Quality Protection Plan Secretariat (2009) Reef Water Quality Protection Plan 2009. For the Great Barrier Reef World Heritage Area and Adjacent Catchments. vol Reef Water Quality Protection Plan Secretariat, Brisbane, Australia
  102. Reef Water Quality Protection Plan Secretariat (2013) Reef Water Quality Protection Plan 2013. Securing the Health and Resilience of the Great Barrier Reef World Heritage Area and Adjacent Catchments. vol Reef Water Quality Protection Plan Secretariat, Brisbane, Australia
  103. Regan H, Colyvan M, Burgman M (2002) A taxonomy and treatment of uncertainty for ecology and conservation biology. Ecol Appl 12:618–628CrossRefGoogle Scholar
  104. Roberts S, Nielsen O, Gray D, Sexton J, Davies G (2015) Anuga user manual release 2.0. Geoscience Australia, SymonstonGoogle Scholar
  105. Sarewitz D, Pielke R Jr (1999) Prediction in science and policy. Technol Soc 21(2):121–133CrossRefGoogle Scholar
  106. Saywell T (2016) Rubicon's F2 deposit is uneconomic: new geological model decimates gold resource. The Northern Miner January 18-January 24, Prospectors and Developers Association of Canada, p 1Google Scholar
  107. Spittlehouse J, Joyce P, Vierck E, Schluter P, Pearson J (2014) Ongoing adverse mental health impact of the earthquake sequence in Christchurch, New Zealand. Aust N Z J Psychiatry 48:756–763CrossRefGoogle Scholar
  108. Sterk R (2014) Independent JORC (2012) Technical Report and Mineral Resource Estimate on the Chatham Rise Project in New ZealandGoogle Scholar
  109. Stirling M et al (2012) National seismic hazard model for New Zealand: 2010 update. Bull Seismol Soc Am 102:1514–1542CrossRefGoogle Scholar
  110. Stone R, Hammer G, Marcussen T (1996) Prediction of global rainfall probabilities using phases of the Southern Oscillation Index. Nature 384:252–255CrossRefGoogle Scholar
  111. Swierczek E, Backe G, Holford S, Tenthorey E, Mitchell A (2015) 3D seismic analysis of complex faulting patterns above the Snapper Field, Gippsland Basin: implications for CO2 storage. Aust J Earth Sci 62:77–94CrossRefGoogle Scholar
  112. Syers JK, Mackay AD, Brown MW, Currie LD (1986) Chemical and physical characteristics of phosphate rock materials of varying reactivity. J Sci Food Agric 37:1057–1064. CrossRefGoogle Scholar
  113. Taylor S, McLennan S (1995) The geochemical evolution of the continental crust. Rev Geophys 33:241–265CrossRefGoogle Scholar
  114. Thayalakumaran T, Barlow K, Moody P ()2015 Extending “SafeGauge for Nutrients” to rainfed dairy systems in Victoria, Australia. In: 21st international congress on modelling and simulation, Gold Coast, Australia, 2015Google Scholar
  115. Thiele S, Jessell M, Lindsay M, Wellmann J, Pakyuz-Charrier E (2016) The topology of geology 2: topological uncertainty. J Struct Geol 91:74–87CrossRefGoogle Scholar
  116. Tremlett J (2015) Project 2058, One Ocean: Principles for the stewardship of a healthy and productive ocean. The McGuinness Institute, as part of Project 2058, Wellington, New ZealandGoogle Scholar
  117. Van den Honert R, McAneney J (2011) The 2011 Brisbane floods: causes, impacts and implications. Water 3:1149–1173. CrossRefGoogle Scholar
  118. Van Dissen R, Hornblow S, Villamor P, Quigley M, Litchfield N, Nicol A, Barrell D Greendale Fault rupture of 2010 (Darfield Earthquake, New Zealand): an example of recurrence interval and ground surface displacement characterisation for land-use planning and engineering design purposes. In: 6th international conference on earthquake geotechnical engineering, Christchurch, New Zealand, 2015Google Scholar
  119. Villamor P et al (2012) Map of the 2010 Greendale Fault surface rupture, Canterbury, New Zealand: application to land use planning. N Z J Geol Geophys 55:223CrossRefGoogle Scholar
  120. Wahl T, Jain S, Bender J, Meyers S, Luther M (2015) Increasing risk of compound flooding from storm surge and rainfall for major US cities. Nature Clim Change 5:1093–1097. CrossRefGoogle Scholar
  121. Wedding LM et al (2015) Managing mining of the deep seabed. Science 349:144–145. CrossRefGoogle Scholar
  122. Wein A, Potter S, Johal S, Doyle E, Becker J (2016) Communicating with the public during an earthquake sequence: improving communication of geoscience by coordinating roles. Seismol Res Lett 87:112–118. CrossRefGoogle Scholar
  123. Wellmann J, Horowitz F, Schill E, Regenauer-Lieb K (2010) Towards incorporating uncertainty of structural data in 3D geological inversion. Tectonophysics 490:141–151CrossRefGoogle Scholar
  124. Wenger C, Hussey K, Pittock J (2013) Living with floods: key lessons from Australia and abroad. National Climate Change Adaptation Research Facility, QLD, AustraliaGoogle Scholar
  125. White C et al (2010) Climate futures for Tasmania: extreme events technical report. Antarctic Climate and Ecosystems Cooperative Research Centre, HobartGoogle Scholar
  126. White C et al (2013) On regional dynamical downscaling for the assessment and projection of temperature and precipitation extremes across Tasmania, Australia. Clim Dyn 41:3145–3165. CrossRefGoogle Scholar
  127. White C, Remenyi T, McEvoy D, Trundle A, Corney S (2016a) 2016 Tasmanian State Natural Disaster Risk Assessment. University of TasmaniaGoogle Scholar
  128. White C, Remenyi T, McEvoy D, Trundle A, Corney S (2016b) 2016 Tasmanian State Natural Disaster Risk Assessment: All Hazard Summary. University of TasmaniaGoogle Scholar
  129. Wilkinson SN, Dougall C, Kinsey-Henderson A, Searle RD, Ellis RJ, Bartley R (2014) Development of a time-stepping sediment budget model for assessing land use impacts in large river basins. Sci Total Environ 468–469:1210–1224CrossRefGoogle Scholar
  130. Wood RA (2014) Statement of evidence of Raymond Allen Wood for Chatham Rock Phosphate LimitedGoogle Scholar
  131. Wood RA, Falconer R (2016) Chatham Phosphate—a strategic resource. In: Christie AB (ed) Mineral Deposits of New Zealand Exploration and Research vol 31. vol AusIMM Monograph 31, vol 31. The Australasian Institute of Mining and Metallurgy, Melbourne, pp 517–522Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Earth SciencesUniversity of MelbourneParkvilleAustralia
  2. 2.School of Mathematical SciencesUniversity of AdelaideAdelaideAustralia
  3. 3.GNS ScienceLower HuttNew Zealand
  4. 4.CSIRO Data61CanberraAustralia
  5. 5.Centre for Exploration Targeting, School of Earth SciencesUniversity of Western AustraliaCrawleyAustralia
  6. 6.School of Agricultural, Computational and Environmental Sciences and Centre for Sustainable Agricultural SystemsUniversity of Southern QueenslandToowoombaAustralia
  7. 7.School of Mathematics and StatisticsUniversity of MelbourneParkvilleAustralia
  8. 8.Australian Rivers InstituteGriffith UniversityNathanAustralia
  9. 9.School of EngineeringUniversity of TasmaniaHobartAustralia
  10. 10.Department of Civil and Environmental EngineeringUniversity of StrathclydeGlasgowUK

Personalised recommendations